Advances in analytical science have made it possible to extract a wide variety of information from a biological specimen. For example, it may be possible to assess the health, diagnose a disease state, identify possible future health issues, predict a response to a treatment, and provide information related to the genetic makeup of an individual from which the specimen was obtained.
Histochemical staining has made it possible to highlight morphological features of a specimen and in some cases to detect and visualize the presence of target molecules with a specimen. For example, immunohistochemical staining, also referred to herein as IHC, utilizes antibody-based detection systems to detect and visualize the presence within a specimen of a protein to which an antibody has been developed.
Moreover, advances in digital microscopic imaging have enabled microscopic images to be captured, processed, and analyzed.
However, analysis of histochemical staining has been largely regarded as non-quantitative or semi-quantitative at best. Image analysis of histochemical staining has been utilized in attempts to make the analyses more quantitative. For example, digital image analysis systems may measure the intensity of staining within predetermined thresholds of color. While such systems may assist, for example, in reducing the variation in scoring between different observers, such analysis systems suffer from the fact that the variance of the shape and size of optically discernible objects within the image of the specimen has been as high as the variance of the inherent shape and size of the features present in the specimen prior to staining, which is typically relatively high.
Thus, some conventional image analysis algorithms have avoided attempting to classify objects according to size and shape, and focused primarily on ratios of different color stains within the specimen. Other image analysis algorithms have attempted to use rather complex object recognition techniques, which again have had to deal with the naturally occurring variance of shape and size of features within the specimen.
Therefore, there has been a need to develop methods and systems for imaging specimens that overcome the limitations and disadvantages of conventional assays and imaging systems.